Coral Holobiont Physiology under OA/OW
Last compiled on 26 April 2021
Principal component analyses
Methods:
Principal component analysis (PCA) (function prcomp) of scaled and centered physiological parameters (host carbohydrate, host lipid, host protein, algal endosymbiont chlorophyll a, algal endosymbiont cell density, holobiont calcification rate as previously for the same samples in Bove et al. (2019)) were employed to assess the relationship between physiological parameters and treatment conditions for each coral species. Main effects (temperature, pCO2, and reef environment) were evaluated with PERMANOVA using the adonis2 function (vegan package; version 2.5.7) (Tables XXX).
Results:
Two principal components (PCs) explained approximately 66% of the variance in physiological responses of the S. siderea holobiont to ocean acidification and warming treatments (Figure 1A). PC1 was driven by differences in algal endosymbiont physiology (chlorophyll a, cell density), while PC2 represented an inverse relationship between host energy reserves (lipid, protein, carbohydrate) and calcification rates and colour intensities. Overall, lower pCO2 and temperature resulted in higher S. siderea holobiont physiology (Figure 1A). Treatment pCO2 predominantly drove S. siderea physiological responses (p < 0.001; Table S2), while temperature and reef environment were not as strong of drivers in physiological responses (p > 0.01 and p > 0.01, respectively; Table S2). For P. strigosa, 74% of the variance in the holobiont responses to treatments was explained by two PCs (Figure 1B). PC1 explained most of the variation of physiological parameters with the exception of host lipid content, which was represented in PC2. Holobiont physiology of P. strigosa was clearly reduced under warming and was generally higher in the lower pCO2 treatments (Figure 1B). Treatment temperature (p < 0.001; Table S2), pCO2 (p < 0.01; Table S2), and natal reef environment all significantly drove coral holobiont physiology (p < 0.001; Table S2). Finally, the first two PCs explained about 59% of the total variance of the P. astreoides holobiont response to treatment (Figure 1C). Coral holobiont samples separated most clearly along PC1 driven primarily by calcification rate and algal endosymbiont density, while PC2 exhibited an inverse relationship between host total carbohydrate and colour intensity. Overall, lower pCO2 drove higher P. astreoides holobiont physiology, while elevated temperature resulted in greater holobiont physiology (Figure 1C). Temperature (p < 0.001; Table S2) and pCO2 (p < 0.001; Table S2) drove separations in P. astreoides holobiont physiology, while reef environment was nonsignificant (p = 0.82; Table S2).
Siderastrea siderea
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1500
##
## adonis2(formula = sid_pca_df ~ fpco2 + ftemp + reef, data = s_df, permutations = bootnum, method = "eu")
## Df SumOfSqs R2 F Pr(>F)
## fpco2 3 150243 0.27232 11.4516 0.0006662 ***
## ftemp 1 24935 0.04520 5.7018 0.0053298 **
## reef 1 26681 0.04836 6.1009 0.0033311 **
## Residual 80 349861 0.63413
## Total 85 551720 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pseudodiploria strigosa
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1500
##
## adonis2(formula = dip_pca_df ~ reef + fpco2 + ftemp, data = p_df, permutations = bootnum, method = "eu")
## Df SumOfSqs R2 F Pr(>F)
## reef 1 162037 0.07959 10.9355 0.0006662 ***
## fpco2 3 196323 0.09644 4.4165 0.0019987 **
## ftemp 1 625389 0.30720 42.2061 0.0006662 ***
## Residual 71 1052041 0.51677
## Total 76 2035789 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Porites astreoides
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1500
##
## adonis2(formula = por_pca_df ~ reef + ftemp + fpco2, data = a_df, permutations = bootnum, method = "eu")
## Df SumOfSqs R2 F Pr(>F)
## reef 1 505 0.00150 0.1692 0.8194537
## ftemp 1 56228 0.16639 18.8252 0.0006662 ***
## fpco2 3 96015 0.28412 10.7153 0.0006662 ***
## Residual 62 185186 0.54799
## Total 67 337935 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Figure 1
Figure 1. Principal component analysis (PCA) of all coral holobiont physiological parameters for S. siderea (A), P. strigosa (B), and P. astreoides (C) after 93 days of exposure to different temperature and pCO2 treatments. PCAs in the top row are depicted by temperature treatment for each species (28\(^\circ\) C blue; 31\(^\circ\) C red) and the bottom row of PCAs are depicted by pCO2 for each species (280 \(\mu\)atm light purple; 400 \(\mu\)atm dark purple; 700 \(\mu\)atm light orange; 2800 \(\mu\)atm dark orange). Arrows represent significant (p < 0.05) correlation vectors for physiological parameters and ellipses represent 95% confidence based on multivariate t-distributions.
Correlation assessments
Methods:
Correlations of all physiological parameters were assessed to determine the relationships between parameters within each species, redargless of temperature and pCO2 treatment. The Pearson correlation coefficient (R2) of each comparison was calculated using the corrgram package (version 1.13) and the significance was calculated using the cor.test function. These relationships were then visualized through simple scatterplots.
Results:
Correlations of coral holobiont physiological parameters were generally positively related with one another across all three species. Correlations between S. siderea holobiont physiological parameters identified 15 significant relationships out of all 21 possible comparisons (Figure 2A). Of those significant correlations, six resulted in a Pearson’s correlation coefficient (R2) equal to or greater than 0.5, with the strongest relationship identified being symbiont density vs chlorophyll a (R2 = 0.72). All pairwise physiological parameters were significantly correlated with one another in P. strigosa and of those, 14 correlations exhibit moderate (R2 > 0.50) positive relationships (Figure 2B). Notably, the two strongest correlations were host carbohydrate vs host protein (R2 = 0.70) and host carbohydrate vs chlorophyll a (R2 = 0.76). Compared to both S. siderea and P. strigosa, fewer physiological traits were significantly (p < 0.05) correlated with one another in P. astreoides (12 significant out of 21 total comparisons; Figure 2C). Of the significant correlations, only two pairwise comparisons resulted in a Pearson’s correlation coefficient greater than 0.5: chlorophyll a vs colour intensity (R2 = 0.57) and host carbohydrate vs host protein (R2 = 0.68).
Figure 2. Coral holobiont correlation matrices (bottom panel) and scatter plots (top panel) for S. siderea (A), P. strigosa (B), and P. astreoides (C) depicting pairwise comparisons of physiological parameters within each species. Strength of correlations between parameters is indicated by darker shades of blue in the bottom panel with a higher R2 value (Pearson correlation coefficient). Of these correlations, significant correlations are depicted with asterisks according to significance level (* p < 0.05; ** p < 0.01; *** p < 0.001). Scatter plots of physiological parameters are displayed in the top panel with temperature depicted by shape (28\(^\circ\)C filled points; 31\(^\circ\)C open points) and pCO2 depicted by colour (280 \(\mu\)atm light purple; 400 \(\mu\)atm dark purple; 700 \(\mu\)atm light orange; 2800 \(\mu\)atm dark orange).
Plasticity analyses
Methods:
Using PC1 and PC2 for each species, we then calculated the phenotypic plasticity of each experimental fragment. Plasticity was calculated as the PC distance between an experimental fragment and the control (400 \(\mu\)atm; 28\(^\circ\)C) fragment from that same colony. The effects of treatment (pCO2 and temperature) and natal reef environment on calculated distances were assessed using generalized linear models (function glm) with a Gamma distribution and log-link. The best-fit model was selected as the model with the lowest AIC for each species (Table SXX). The main effects of treatment and reef environment were assessed with an ANOVA (package car; version 3.0.10) with a type III error. All figures and statistical analyses were carried out in R version 3.6.3 (R Core Development Team 2016).
Results:
Natal reef environment (p < 0.05) and pCO2 (p < 0.05) significantly altered the phenotypic plasticity of S. siderea (Figure 3A; Table S3, S4). Offshore fragments exhibited a positive linear trend with increasing pCO2 while the inshore fragments appear to respond in a parabolic pattern to pCO2, with the lowest calculated distances occurring at 400 \(\mu\)atm, 31\(^\circ\)C and 700 \(\mu\)atm, 28\(^\circ\)C. Plasticity of P. strigosa and P. astreoides was not significantly altered by temperature treatment, pCO2 treatment, or natal reef environment (Figure 3B, 3C; Table S3, S4). However, P. astreoides exhibited a slight trend in the inshore fragments suggesting potentially higher plasticity with increasing pCO2 that is not seen in the offshore fragments (Figure 3C).
Siderastrea siderea
##
## Call:
## glm(formula = dist ~ reef * fpco2 + ftemp, family = Gamma(link = "log"),
## data = sid_dist, contrasts = list(reef = contr.sum, fpco2 = contr.sum,
## ftemp = contr.sum))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5392 -0.4066 -0.1219 0.2076 1.6019
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.75433 0.08433 8.945 5.45e-13 ***
## reef1 -0.20433 0.08271 -2.470 0.0161 *
## fpco21 -0.11652 0.14635 -0.796 0.4288
## fpco22 -0.13498 0.18076 -0.747 0.4579
## fpco23 -0.12823 0.13205 -0.971 0.3351
## ftemp1 -0.02907 0.08563 -0.339 0.7353
## reef1:fpco21 0.30174 0.14331 2.106 0.0390 *
## reef1:fpco22 -0.24282 0.16729 -1.451 0.1514
## reef1:fpco23 -0.04294 0.13049 -0.329 0.7431
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.4613617)
##
## Null deviance: 36.807 on 74 degrees of freedom
## Residual deviance: 27.862 on 66 degrees of freedom
## AIC: 254.68
##
## Number of Fisher Scoring iterations: 6## Analysis of Deviance Table (Type III tests)
##
## Response: dist
## Error estimate based on Pearson residuals
##
## Sum Sq Df F values Pr(>F)
## reef 2.7883 1 6.0436 0.01659 *
## fpco2 4.3615 3 3.1512 0.03064 *
## ftemp 0.0514 1 0.1114 0.73965
## reef:fpco2 2.2148 3 1.6002 0.19774
## Residuals 30.4499 66
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Pseudodiploria strigosa
##
## Call:
## glm(formula = dist ~ reef + fpco2 + ftemp, family = Gamma(link = "log"),
## data = dip_dist, contrasts = list(reef = contr.sum, fpco2 = contr.sum,
## ftemp = contr.sum))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.42095 -0.58130 -0.06819 0.29076 1.27882
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.84433 0.09178 9.199 1.93e-13 ***
## reef1 0.04349 0.07514 0.579 0.565
## fpco21 0.08559 0.12845 0.666 0.508
## fpco22 0.07124 0.23272 0.306 0.760
## fpco23 -0.11009 0.14111 -0.780 0.438
## ftemp1 -0.03461 0.08164 -0.424 0.673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.3908735)
##
## Null deviance: 27.294 on 71 degrees of freedom
## Residual deviance: 26.566 on 66 degrees of freedom
## AIC: 242.16
##
## Number of Fisher Scoring iterations: 6## Analysis of Deviance Table (Type III tests)
##
## Response: dist
## Error estimate based on Pearson residuals
##
## Sum Sq Df F values Pr(>F)
## reef 0.1283 1 0.3283 0.5686
## fpco2 0.4785 3 0.4081 0.7477
## ftemp 0.0675 1 0.1727 0.6791
## Residuals 25.7977 66Porites astreoides
##
## Call:
## glm(formula = dist ~ reef + fpco2 + ftemp, family = Gamma(link = "log"),
## data = por_dist)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3919 -0.4125 -0.0464 0.2664 1.1198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.65338 0.16238 4.024 0.00019 ***
## reefF 0.02488 0.14303 0.174 0.86260
## fpco2420 -0.16668 0.26763 -0.623 0.53620
## fpco2680 0.12920 0.17739 0.728 0.46975
## fpco23300 0.20985 0.18424 1.139 0.26003
## ftemp31 0.30231 0.15766 1.917 0.06079 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.2725499)
##
## Null deviance: 16.944 on 56 degrees of freedom
## Residual deviance: 15.563 on 51 degrees of freedom
## AIC: 188.39
##
## Number of Fisher Scoring iterations: 5## Analysis of Deviance Table (Type III tests)
##
## Response: dist
## Error estimate based on Pearson residuals
##
## Sum Sq Df F values Pr(>F)
## reef 0.0082 1 0.0300 0.86317
## fpco2 0.6725 3 0.8225 0.48753
## ftemp 1.0393 1 3.8133 0.05635 .
## Residuals 13.9000 51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Figure 3
Figure 3. Assessment of phenotypic plasticity of S. siderea (A), P. strigosa (B), and P. astreoides (C) in experimental treatments and by natal reef environment. Higher values represent greater plasticity in coral holobiont samples. pCO2 treatment is depicted by colour and shape (280 \(\mu\)atm light purple, circle; 400 \(\mu\)atm dark purple, diamond; 700 \(\mu\)atm light orange, triangle; 2800 \(\mu\)atm dark orange, square) and temperature is represented as either filled (28\(^\circ\)C) or open (31\(^\circ\)C) symbols.
Physiological paramters
Physiological response parameters were assessed using mixed-effects linear models across species and treatments. Model selection was carried out using backward elimination of random-effects followed by fixed-effects using the package lmerTest (version 3.1.3)
Protein
While value ~ species + fpco2 + ftemp + (1 | colony) + species:ftemp was the best-fit model structure identified, we wanted to model both pCO2 and temperature responses by species so moving forward we are using the following model structure:
value ~ species * (fpco2 + ftemp) + (1 | colony)
Figure:
Carbohydrate
While value ~ species + ftemp was the best-fit model structure identified, we wanted to model responses with a random effect of colony so moving forward we are using the following model structure:
value ~ species * (fpco2 + ftemp) + (1 | colony)
Figure:
Lipid
While value ~ species + ftemp + reef + species:ftemp + species:reef was the best-fit model structure identified, we wanted to model both pCO2 and temperature responses by species and with random effect of colony so moving forward we are using the following model structure:
value ~ species * (fpco2 + ftemp) + reef + species:reef + (1 | colony)
Figure:
Density
While value ~ species + fpco2 + ftemp + reef + (1 | colony) + species:fpco2 + species:ftemp + fpco2:ftemp + species:reef + fpco2:reef + ftemp:reef + species:fpco2:ftemp + species:fpco2:reef + species:ftemp:reef + fpco2:ftemp:reef + species:fpco2:ftemp:reef was the best-fit model structure identified, we wanted to model both pCO2 and temperature responses by species and with random effect of colony so moving forward we are using the following model structure:
value ~ species * (fpco2 + ftemp) + reef + species:reef + (1 | colony)
Figure:
Chlorophyll
Since the best-fit model fits our design, we will proceed with the following model structure:
value ~ species + fpco2 + ftemp + reef + (1 | colony) + species:fpco2 + species:ftemp + fpco2:ftemp + species:reef
Figure:
Total Host
Since the best-fit model fits our design, we will proceed with the following model structure:
value ~ species + fpco2 + ftemp + reef + (1 | colony) + species:fpco2 + species:ftemp + species:reef + fpco2:reef + species:fpco2:reef
Figure:
Calcification
This is the same model from Bove et al 2019, just matching aesthetics for this manuscript.
Figure S1
Figure S1. Modeled 95% confidence interval of (A) host total protein (mg cm-2), (B) host total carbohydrate (mg cm-2), (C) host total lipid (mg cm-2), (D) cell density (106 cells cm-2), and (E) Chlorophyll a (ug cm-2) for S. siderea (left), P. strigosa (center), and P. astreoides (right) at T0 (green) or T90 (red/blue), with individual coral fragment physiology denoted by points. Blue denotes 28°C and red denotes 31°C, with pCO2 treatment along the x axis.
S. siderea gene expression subset
SSID subset PCA
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 1500
##
## adonis2(formula = s_df_sub[, c(14:17, 21:23, 29)] ~ fpco2 + fpco2:domSymb + domSymb + ftemp, data = s_df_sub, permutations = bootnum, method = "eu")
## Df SumOfSqs R2 F Pr(>F)
## fpco2 3 60040 0.26137 12.2655 0.0006662 ***
## domSymb 2 76699 0.33389 23.5033 0.0006662 ***
## ftemp 1 13933 0.06065 8.5391 0.0013324 **
## fpco2:domSymb 6 28458 0.12389 2.9068 0.0039973 **
## Residual 31 50582 0.22020
## Total 43 229711 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Siderastrea siderea subset
Supplemental Tables
| treatment | N | mean | lower 95% | upper 95% |
|---|---|---|---|---|
| (a) SSID | ||||
| 300_28 | 11 | 0.54 | 0.47 | 0.60 |
| 300_31 | 9 | 0.48 | 0.41 | 0.55 |
| 3300_28 | 12 | 0.43 | 0.37 | 0.50 |
| 3300_31 | 12 | 0.38 | 0.31 | 0.44 |
| 420_28 | 12 | 0.50 | 0.43 | 0.57 |
| 420_31 | 12 | 0.45 | 0.38 | 0.51 |
| 680_28 | 13 | 0.46 | 0.39 | 0.53 |
| 680_31 | 12 | 0.40 | 0.34 | 0.47 |
| (b) PSTR | ||||
| 300_28 | 16 | 0.53 | 0.47 | 0.59 |
| 300_31 | 9 | 0.27 | 0.19 | 0.35 |
| 3300_28 | 16 | 0.43 | 0.36 | 0.49 |
| 3300_31 | 8 | 0.17 | 0.09 | 0.24 |
| 420_28 | 5 | 0.49 | 0.42 | 0.56 |
| 420_31 | 6 | 0.23 | 0.15 | 0.32 |
| 680_28 | 14 | 0.45 | 0.39 | 0.51 |
| 680_31 | 5 | 0.19 | 0.11 | 0.28 |
| (c) PAST | ||||
| 300_28 | 11 | 0.23 | 0.17 | 0.30 |
| 300_31 | 6 | 0.19 | 0.10 | 0.27 |
| 3300_28 | 12 | 0.13 | 0.06 | 0.20 |
| 3300_31 | 4 | 0.08 | 0.00 | 0.17 |
| 420_28 | 12 | 0.20 | 0.13 | 0.26 |
| 420_31 | 7 | 0.15 | 0.07 | 0.23 |
| 680_28 | 10 | 0.16 | 0.09 | 0.22 |
| 680_31 | 9 | 0.11 | 0.03 | 0.19 |
| treatment | N | mean | lower 95% | upper 95% |
|---|---|---|---|---|
| (a) SSID | ||||
| 300_28 | 11 | 0.37 | 0.30 | 0.44 |
| 300_31 | 9 | 0.35 | 0.28 | 0.42 |
| 3300_28 | 12 | 0.37 | 0.30 | 0.44 |
| 3300_31 | 12 | 0.35 | 0.28 | 0.42 |
| 420_28 | 12 | 0.37 | 0.30 | 0.44 |
| 420_31 | 12 | 0.35 | 0.27 | 0.42 |
| 680_28 | 13 | 0.38 | 0.31 | 0.45 |
| 680_31 | 12 | 0.36 | 0.29 | 0.43 |
| (b) PSTR | ||||
| 300_28 | 16 | 0.24 | 0.17 | 0.31 |
| 300_31 | 9 | 0.11 | 0.02 | 0.20 |
| 3300_28 | 15 | 0.24 | 0.17 | 0.31 |
| 3300_31 | 8 | 0.10 | 0.02 | 0.19 |
| 420_28 | 5 | 0.24 | 0.17 | 0.31 |
| 420_31 | 5 | 0.11 | 0.02 | 0.20 |
| 680_28 | 14 | 0.24 | 0.17 | 0.31 |
| 680_31 | 5 | 0.11 | 0.02 | 0.20 |
| (c) PAST | ||||
| 300_28 | 11 | 0.15 | 0.08 | 0.23 |
| 300_31 | 6 | 0.20 | 0.11 | 0.29 |
| 3300_28 | 12 | 0.16 | 0.08 | 0.23 |
| 3300_31 | 4 | 0.22 | 0.14 | 0.31 |
| 420_28 | 12 | 0.16 | 0.08 | 0.23 |
| 420_31 | 7 | 0.20 | 0.11 | 0.29 |
| 680_28 | 10 | 0.16 | 0.08 | 0.23 |
| 680_31 | 9 | 0.20 | 0.11 | 0.29 |
| treatment | N | mean | lower 95% | upper 95% |
|---|---|---|---|---|
| (a) SSID | ||||
| 300_28 | 11 | 1.15 | 0.95 | 1.35 |
| 300_31 | 8 | 0.82 | 0.61 | 1.02 |
| 3300_28 | 12 | 1.10 | 0.91 | 1.29 |
| 3300_31 | 12 | 0.77 | 0.60 | 0.96 |
| 420_28 | 12 | 1.08 | 0.90 | 1.26 |
| 420_31 | 12 | 0.75 | 0.57 | 0.94 |
| 680_28 | 13 | 1.27 | 1.10 | 1.45 |
| 680_31 | 12 | 0.94 | 0.75 | 1.11 |
| (b) PSTR | ||||
| 300_28 | 16 | 0.77 | 0.60 | 0.93 |
| 300_31 | 9 | 0.50 | 0.30 | 0.68 |
| 3300_28 | 16 | 0.62 | 0.45 | 0.78 |
| 3300_31 | 8 | 0.34 | 0.15 | 0.55 |
| 420_28 | 5 | 0.67 | 0.41 | 0.92 |
| 420_31 | 6 | 0.40 | 0.16 | 0.65 |
| 680_28 | 14 | 0.56 | 0.37 | 0.75 |
| 680_31 | 7 | 0.29 | 0.07 | 0.51 |
| (c) PAST | ||||
| 300_28 | 11 | 0.82 | 0.61 | 1.03 |
| 300_31 | 6 | 0.65 | 0.41 | 0.88 |
| 3300_28 | 12 | 0.58 | 0.38 | 0.78 |
| 3300_31 | 4 | 0.41 | 0.16 | 0.65 |
| 420_28 | 12 | 0.90 | 0.71 | 1.10 |
| 420_31 | 7 | 0.73 | 0.51 | 0.95 |
| 680_28 | 10 | 0.61 | 0.41 | 0.81 |
| 680_31 | 9 | 0.43 | 0.23 | 0.64 |
| treatment | N | mean | lower 95% | upper 95% |
|---|---|---|---|---|
| (a) SSID | ||||
| 300_28 | 11 | 3.32 | 2.23 | 4.46 |
| 300_31 | 9 | 2.45 | 1.33 | 3.58 |
| 3300_28 | 12 | 2.04 | 0.97 | 3.07 |
| 3300_31 | 12 | 1.18 | 0.12 | 2.23 |
| 420_28 | 12 | 3.48 | 2.42 | 4.50 |
| 420_31 | 12 | 2.61 | 1.55 | 3.67 |
| 680_28 | 13 | 2.96 | 1.95 | 3.98 |
| 680_31 | 12 | 2.10 | 1.04 | 3.14 |
| (b) PSTR | ||||
| 300_28 | 16 | 2.16 | 1.14 | 3.15 |
| 300_31 | 9 | 0.42 | -0.77 | 1.60 |
| 3300_28 | 16 | 1.53 | 0.53 | 2.52 |
| 3300_31 | 8 | -0.27 | -1.48 | 0.89 |
| 420_28 | 5 | 2.16 | 0.75 | 3.61 |
| 420_31 | 6 | 0.45 | -0.96 | 1.86 |
| 680_28 | 14 | 1.71 | 0.68 | 2.75 |
| 680_31 | 7 | -0.09 | -1.30 | 1.14 |
| (c) PAST | ||||
| 300_28 | 11 | 7.29 | 6.13 | 8.48 |
| 300_31 | 6 | 6.42 | 5.02 | 7.74 |
| 3300_28 | 12 | 5.92 | 4.74 | 7.16 |
| 3300_31 | 4 | 4.86 | 3.51 | 6.15 |
| 420_28 | 12 | 6.43 | 5.28 | 7.57 |
| 420_31 | 6 | 5.51 | 4.22 | 6.83 |
| 680_28 | 10 | 5.09 | 3.84 | 6.35 |
| 680_31 | 8 | 4.19 | 2.87 | 5.45 |
| treatment | N | mean | lower 95% | upper 95% |
|---|---|---|---|---|
| (a) SSID | ||||
| 300_28 | 11 | 112.38 | 81.74 | 143.62 |
| 300_31 | 9 | 105.47 | 71.18 | 140.02 |
| 3300_28 | 12 | 48.52 | 17.15 | 79.21 |
| 3300_31 | 12 | 32.61 | 3.07 | 63.16 |
| 420_28 | 12 | 155.21 | 122.77 | 186.81 |
| 420_31 | 12 | 77.84 | 46.62 | 108.58 |
| 680_28 | 13 | 83.24 | 53.49 | 114.40 |
| 680_31 | 12 | 82.41 | 51.78 | 113.66 |
| (b) PSTR | ||||
| 300_28 | 16 | 185.93 | 157.24 | 214.55 |
| 300_31 | 9 | 120.37 | 85.65 | 154.64 |
| 3300_28 | 16 | 78.53 | 51.36 | 106.93 |
| 3300_31 | 8 | -1.42 | -37.11 | 34.23 |
| 420_28 | 5 | 161.17 | 118.71 | 202.49 |
| 420_31 | 6 | 26.74 | -14.58 | 66.79 |
| 680_28 | 14 | 84.10 | 54.62 | 114.41 |
| 680_31 | 5 | 17.96 | -22.30 | 58.03 |
| (c) PAST | ||||
| 300_28 | 11 | 97.02 | 63.84 | 130.54 |
| 300_31 | 6 | 155.01 | 116.85 | 192.29 |
| 3300_28 | 12 | 15.56 | -18.42 | 45.96 |
| 3300_31 | 4 | 61.04 | 19.29 | 101.45 |
| 420_28 | 12 | 64.66 | 33.23 | 97.25 |
| 420_31 | 7 | 51.82 | 15.19 | 89.83 |
| 680_28 | 10 | 33.69 | 1.31 | 67.61 |
| 680_31 | 9 | 96.83 | 62.24 | 133.28 |
| treatment | N | mean | lower 95% | upper 95% |
|---|---|---|---|---|
| (a) SSID | ||||
| 300_28 | 11 | 2.02 | 1.61 | 2.43 |
| 300_31 | 8 | 1.62 | 1.21 | 2.04 |
| 3300_28 | 12 | 1.92 | 1.56 | 2.28 |
| 3300_31 | 12 | 1.53 | 1.17 | 1.89 |
| 420_28 | 12 | 2.02 | 1.65 | 2.40 |
| 420_31 | 12 | 1.58 | 1.21 | 1.95 |
| 680_28 | 13 | 2.06 | 1.70 | 2.41 |
| 680_31 | 12 | 1.65 | 1.29 | 2.01 |
| (b) PSTR | ||||
| 300_28 | 16 | 1.60 | 1.25 | 1.94 |
| 300_31 | 9 | 0.96 | 0.58 | 1.35 |
| 3300_28 | 15 | 1.28 | 0.92 | 1.64 |
| 3300_31 | 8 | 0.60 | 0.20 | 0.99 |
| 420_28 | 5 | 1.39 | 0.84 | 1.94 |
| 420_31 | 5 | 0.71 | 0.14 | 1.27 |
| 680_28 | 14 | 1.23 | 0.82 | 1.62 |
| 680_31 | 5 | 0.56 | 0.11 | 1.00 |
| (c) PAST | ||||
| 300_28 | 11 | 1.26 | 0.84 | 1.69 |
| 300_31 | 6 | 1.12 | 0.65 | 1.60 |
| 3300_28 | 12 | 0.86 | 0.41 | 1.29 |
| 3300_31 | 4 | 0.56 | 0.13 | 0.98 |
| 420_28 | 12 | 1.26 | 0.86 | 1.66 |
| 420_31 | 7 | 1.14 | 0.71 | 1.58 |
| 680_28 | 10 | 0.85 | 0.44 | 1.25 |
| 680_31 | 9 | 0.72 | 0.31 | 1.14 |
Session information
Session information from the last run date on 2021-04-26:
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] car_3.0-10 carData_3.0-4 png_0.1-7 MASS_7.3-53
## [5] performance_0.7.0 wesanderson_0.3.6 RColorBrewer_1.1-2 gridGraphics_0.5-1
## [9] corrplot_0.84 Hmisc_4.4-2 Formula_1.2-4 survival_3.2-7
## [13] magick_2.5.2 ggpubr_0.4.0 vroom_1.3.2 lmerTest_3.1-3
## [17] lme4_1.1-26 Matrix_1.3-2 kableExtra_1.3.1 finalfit_1.0.2
## [21] ggfortify_0.4.11 cowplot_1.1.1 Rmisc_1.5 shiny_1.5.0
## [25] vegan_2.5-7 lattice_0.20-41 permute_0.9-5 forcats_0.5.0
## [29] stringr_1.4.0 purrr_0.3.4 tibble_3.0.4 tidyverse_1.3.0
## [33] plotly_4.9.3 openxlsx_4.2.3 corrgram_1.13 tidyr_1.1.2
## [37] ggbiplot_0.55 scales_1.1.1 plyr_1.8.6 dplyr_1.0.2
## [41] ggplot2_3.3.3 readr_1.4.0 knitr_1.30
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.2.1 lazyeval_0.2.2
## [4] splines_3.6.3 digest_0.6.27 foreach_1.5.1
## [7] htmltools_0.5.1 fansi_0.4.1 magrittr_2.0.1
## [10] checkmate_2.0.0 cluster_2.1.0 see_0.6.2
## [13] modelr_0.1.8 jpeg_0.1-8.1 colorspace_2.0-0
## [16] ggrepel_0.9.0 rvest_0.3.6 haven_2.3.1
## [19] xfun_0.20 crayon_1.3.4 jsonlite_1.7.2
## [22] iterators_1.0.13 glue_1.4.2 registry_0.5-1
## [25] gtable_0.3.0 webshot_0.5.2 abind_1.4-5
## [28] DBI_1.1.0 rstatix_0.6.0 Rcpp_1.0.5
## [31] viridisLite_0.3.0 xtable_1.8-4 htmlTable_2.1.0
## [34] foreign_0.8-75 bit_4.0.4 htmlwidgets_1.5.3
## [37] httr_1.4.2 ellipsis_0.3.1 mice_3.13.0
## [40] farver_2.0.3 pkgconfig_2.0.3 nnet_7.3-14
## [43] dbplyr_2.0.0 effectsize_0.4.1 labeling_0.4.2
## [46] tidyselect_1.1.0 rlang_0.4.10 later_1.1.0.1
## [49] munsell_0.5.0 cellranger_1.1.0 tools_3.6.3
## [52] cli_2.2.0 generics_0.1.0 ggridges_0.5.3
## [55] broom_0.7.3 evaluate_0.14 fastmap_1.0.1
## [58] yaml_2.2.1 bit64_4.0.5 fs_1.5.0
## [61] zip_2.1.1 nlme_3.1-151 mime_0.9
## [64] xml2_1.3.2 compiler_3.6.3 rstudioapi_0.13
## [67] curl_4.3 ggsignif_0.6.0 reprex_0.3.0
## [70] statmod_1.4.35 stringi_1.5.3 highr_0.8
## [73] parameters_0.10.1 nloptr_1.2.2.2 vctrs_0.3.6
## [76] pillar_1.4.7 lifecycle_0.2.0 insight_0.13.1
## [79] data.table_1.13.6 seriation_1.2-9 httpuv_1.5.5
## [82] R6_2.5.0 latticeExtra_0.6-29 promises_1.1.1
## [85] TSP_1.1-10 gridExtra_2.3 rio_0.5.16
## [88] codetools_0.2-18 boot_1.3-25 assertthat_0.2.1
## [91] withr_2.3.0 bayestestR_0.8.0 mgcv_1.8-33
## [94] parallel_3.6.3 hms_1.0.0 rpart_4.1-15
## [97] minqa_1.2.4 rmarkdown_2.6 numDeriv_2016.8-1.1
## [100] lubridate_1.7.9.2 base64enc_0.1-3